When TEDDY meets GrizzLY: Temporal Dependency Discovery for Triggering Road Deicing Operations (Demo) - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

When TEDDY meets GrizzLY: Temporal Dependency Discovery for Triggering Road Deicing Operations (Demo)

Céline Robardet
Vasile-Marian Scuturici
Marc Plantevit
Antoine Fraboulet
  • Fonction : Auteur

Résumé

Temporal dependencies between multiple sensor data sources link two types of events if the occurrence of one is repeatedly followed by the appearance of the other in a certain time interval. TEDDY algorithm aims at discovering such dependencies, identifying the statically significant time intervals with a $\chi^2$ test. We present how these dependencies can be used within the GrizzLY project to tackle an environmental and technical issue: the deicing of the roads. This project aims to wisely organize the deicing operations of an urban area, based on several sensor network measures of local atmospheric phenomena. A spatial and temporal dependency-based model is built from these data to predict freezing alerts.
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Dates et versions

hal-01339189 , version 1 (29-06-2016)

Identifiants

Citer

Céline Robardet, Vasile-Marian Scuturici, Marc Plantevit, Antoine Fraboulet. When TEDDY meets GrizzLY: Temporal Dependency Discovery for Triggering Road Deicing Operations (Demo). KDD, Aug 2013, Chicago, IL, United States. pp.1490-1493 ⟨10.1145/2487575.2487706⟩. ⟨hal-01339189⟩
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